Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework
Abstract
Constraint-based causal discovery is brittle in finite-sample regimes because erroneous conditional-independence (CI) decisions can cascade into substantial structural errors. We propose Quantitative Argumentation for Causal Discovery (QACD), a semantics-driven framework that represents CI outcomes as graded, defeasible arguments rather than irreversible constraints. QACD maps statistical test outcomes to argument strengths and aggregates conflicting evidence through connectivity-mediated witness propagation, producing a fixed-point acceptability labeling over candidate adjacencies. Experiments on standard benchmark Bayesian networks suggest that QACD improves structural coherence and interventional reliability in several noisy or inconsistent CI regimes, while remaining competitive with classical constraint-based, hybrid, and prior argumentation-based baselines.
Cite
@article{arxiv.2604.23633,
title = {Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework},
author = {Sheng Wei and Yulin Chen and Beishui Liao},
journal= {arXiv preprint arXiv:2604.23633},
year = {2026}
}
Comments
Accepted at the 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026). This arXiv version includes supplementary material and additional implementation details